InfluxData Product Overview

At InfluxData, we deliver a complete Open Source Platform built from the ground up for metrics, events, and other time-based data—a modern Time Series Platform. We did not take the simple path and just “bolt” time series support onto a SQL or NoSQL store. Instead, we created a platform that is purpose-built for time series data that allows you to focus on your project.

Why a Purpose-Built Time Series Platform?

Compute infrastructure and architectures evolve based on new demands and needs. Existing technologies are often just not good enough to meet these new requirements. Consider Big Data and the advent of HDFS and Hadoop: a whole new category and marketplace were created because the prior technology of data storage in SQL and noSQL stores was inadequate to meet these new demands. No one would seriously consider running their data lake on a SQL database—the same is true for time series data. No one should consider storing time series data in anything but a Time Series Database. This is why we created a purpose-built, modern time series platform.

Fastest Growing Database – Time Series Databases

The category of Time Series Databases (TSDB’s) has been the fastest growing database category for the last two years in a row, according to DB-Engines. This growth is being fueled by two major industry trends—the rapid instrumentation of the physical world driven by increasing investment in IoT systems, and the explosion in the software world of cloud-native applications and services, all of which are being instrumented for real-time visibility and control. This “Age of Instrumentation” is fueling the growth for purpose-built Time Series Platforms that can support the critical requirement for real-time processing of the myriad metrics and events that deliver insight and competitive advantage to data-driven organizations.

Requirements for a Time Series Database

Time Series Databases have to deal with specific workloads and requirements. They need to ingest millions of data points per second; to perform real-time queries across these large data sets in a non-blocking manner; to downsample and evict high-precision low-value data; to optimize data storage to reduce storage costs; and to perform complex time-bound queries to extract meaningful insight from the data. It is possible to meet these requirements only with a purpose-built platform that InfluxData provides.

Functional Architecture

The InfluxData Platform is a complete platform for handling all time series data, from humans, sensors, or machines—seamlessly collecting, storing, visualizing, and turning insight into action. With both fast deployment and fast performance, InfluxData delivers real value in real time. InfluxData has three major product offerings: InfluxCloud (fully managed and hosted service offering), InfluxEnterprise (software that can run on-premises or on any cloud provider), and an open source Time Series Platform.

Accumulate

InfluxData provides a comprehensive set of tools and services to get metrics and events data from sensors, devices, systems, machines, containers, and applications. InfluxData’s collection services are built from the open source Telegraf project. InfluxData provides a number of integrations to popular databases, containers, services, applications, and other monitoring and alerting products. InfluxData provides both a push and pull-based model for metric collection.

Analyze

InfluxData supports real-time stream processing of the data and storage of the time-series data. The data is stored in the leading Time Series Database, InfluxDB. It handles high write loads, large data set storage, and conserves space through downsampling, automatically expiring and deleting unwanted data as well as backup and restore. Analysis of data is done via a SQL-like query language.

You can graph and visualize your data with integrated open source project Chronograf, and perform ad hoc exploration of your data. It includes support for templates and a library of intelligent, pre-configured dashboards for common data sets. In addition, InfluxDB supports other visualization tools such as Grafana.

Act

Naturally, InfluxData supports visualization and monitoring, but today’s modern applications require actions. InfluxData uses the open source Kapacitor project to plug in custom logic or user-defined functions to process alerts with dynamic thresholds, match metrics for patterns or compute statistical anomalies, automatically scale containers, and basically do anything that you can program. It can perform these analytics on streaming as well as data stored in the database.

InfluxData Differentiators

Today, there are different general-purpose products to handle time-based data. But they slow you down with dependencies and complexities or fail to scale. Some have been added to existing products as afterthoughts, yet in contrast, InfluxData was built with time series in mind and is the only purpose-built complete platform. With over 148,000 actively running servers and more than 420 customers, InfluxData is leading this category due to the platform’s unique differentiators:

Fastest Time to Awesome

An end-to-end platform, InfluxData is ready to go in the cloud or via download. Deploy and begin building your application right on top in minutes, not days or weeks. Built to maximize developer happiness, InfluxData is elegant and simple to use, free of external dependencies, yet open and flexible enough for complex deployments. Get real value faster.

Real Action in Real-Time

InfluxData gives you visibility with real-time access out of the gate so you can quickly find value in your data—identify patterns, predict the future, control systems, and turn insight into action. Whether your data comes from humans, machines, or sensors, get instant insights and stay ahead of the curve.

Fast, Scalable, Available

InfluxData’s powerful engine is fast, supports millions of writes per second, and can meet the demands of even the largest monitoring and IoT deployments. And with native clustering, InfluxData offers high availability—eliminating single points of failure—and simple scale-out.

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